Link to the Google Colab code to the main code: https://colab.research.google.com/drive/1T-9GJBWCgE5QxDgEgSJ6p9YQYcYosTx7?usp=sharing
Link to the Google Colab code to the deployment based code: https://colab.research.google.com/drive/1KHUlQZHDxMm9iEGz2TLFJQt6w9K5AKBD#scrollTo=SaketWGJWZ6D
Here, I've created models which predict whether a given sentence contains some hate or offensive overtone. The logistic regression classifier has done well in both the cases.
This model has been deployed using Flask. Other frameworks used include
- HTML
- CSS
- Python
However, in the jupyter notebook, the models are pickled and available in this repository. The other methods I have used here (only in the jupyter notebook) include: Naive Bayes, Decision Tree, KNN and Random Forest.
The main page would take in the user input and once we give in an input, click on predict. (Refer page_1.jpg)
Then, based on the input, the output would be provided. (Refer page_2.jpg and page_3.jpg)